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MPI Elite Endowment Tracker

Name	Endowment	MPI360 Report	Reported	AUM ($B)	2024 return	2023 return	2022 return	2021 return	3Y return	5Y return	10Y return	20Y return	Risk (current)	Private equity %	Sharpe Ratio	Risk (Historical)	10Y Stdev Annual	Assets	Fiscal Year-end	3Q2022 Estimate	4Q2022 Estimate	1Q2023 Estimate	2Q2023 Estimate	FY2023 Estimate	Num
Brown	Brown	PDF	10/04/2024	7.2	11.3	2.7	-4.6	51.5	2.9	13.1	10.8	9.6	22.0	39	0.61	20.1	15.4	12.000000	June	-3.59	0.46	5.69	5.3	8.11	1
Columbia	Columbia	PDF	09/27/2024	14.8	11.5	4.7	-7.6	32.3	2.6	8.5	7.5	9.2	13.1	23	0.68	11.9	10.5	17.204651	June	-3.89	4.29	3.30	3.4	7.90	2
Cornell	Cornell	PDF	10/15/2024	10.7	8.7	3.6	-1.3	41.9	3.6	10.0	7.7	8.0	17.0	45	0.55	15.9	12.8	14.628739	June	-3.14	2.31	2.32	2.5	4.11	3
Dartmouth	Dartmouth	PDF	09/19/2024	8.3	8.4	1.6	-3.1	46.5	2.2	11.0	9.5	9.4	20.0	38	0.61	18.7	14.0	12.884099	June	-3.24	1.68	5.20	5.3	8.99	4
Harvard	Harvard	PDF	10/17/2024	53.2	9.6	2.9	-1.8	33.6	3.5	9.7	7.6	8.2	16.1	34	0.59	14.0	10.0	32.619013	June	-3.70	0.37	3.96	2.6	2.75	5
MIT	MIT	PDF	10/11/2024	24.6	8.9	-2.9	-5.3	55.5	0.1	11.0	10.5	10.7	24.3	18	0.61	21.6	16.9	22.181073	June	-3.18	0.47	3.79	3.9	5.55	6
Princeton	Princeton	PDF	10/24/2024	34.1	3.9	-1.7	-1.5	46.9	0.2	9.3	9.2	9.9	19.8	42	0.64	17.9	14.2	26.115130	June	-2.48	0.94	1.68	2.2	3.45	7
Stanford	Stanford	PDF	10/10/2024	37.6	8.4	4.4	-4.2	40.1	2.7	9.9	8.7	9.3	15.9	37	0.64	14.4	12.0	27.422618	June	-3.37	2.71	3.06	3.1	6.42	8
UPenn	Upenn	PDF	09/26/2024	22.3	7.1	1.5	0.0	41.1	2.8	9.7	8.7	8.4	16.6	36	0.66	15.4	12.3	21.118712	June	-1.92	3.28	2.58	4.0	9.06	9
Yale	Yale	PDF	10/25/2024	41.4	5.7	1.8	0.8	40.2	2.8	10.2	9.5	10.3	16.9	37	0.69	15.3	11.4	28.774989	June	-2.13	0.75	2.87	3.1	5.29	10
Ivy Average	Ivy Average		10/25/2024	24.0	8.3	2.1	-2.4	41.8	2.5	10.2	8.9	9.2	17.4		0.64	15.8	12.4	21.908902	June	-3.30	2.51	4.02	3.9	7.13	11
70-30 Global Bmk	70-30 Global Bmk		06/30/2024		14.2	11.1	-14.0	26.4	3.0	7.7	6.5	6.8	11.8		0.59	11.0	10.6	14.142136	June	-6.35	8.20	6.34	4.8	12.86	12

Fiscal years ending June; 3-year, 5-year, 10-year and 20-year returns are annualized. Risk estimates are standard deviation estimates based on either historical (“Risk (Historical)” column) or the most recent (“Risk (current)” column) estimated fund exposures (obtained through dynamic analysis of annual returns) and quarterly returns of the explanatory risk factors over a 10Y trailing window. “10Y Stdev Annual” is standard deviation of endowments’ 10 trailing reported annual returns, rather than the Lab’s estimates (“Risk (Historical)”).
70-30 Global Benchmark is a quarterly rebalanced portfolio of 70% MSCI ACWI Index and 30% Bloomberg Barclay Aggregate Bond Index. Benchmark standard deviations are computed using quarterly returns to make them comparable to our risk estimation methodology.

10/28/2024: NEW! Elite endowments’ reports, performance, risk and efficiency numbers have been updated with FY2024 results.
07/15/2024: FY2023 Liquidity analysis added to the Elite Endowment Tracker.

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Num	Asset Class	Index Proxy	FY2022	FY2023	FY2024
1	US Bonds	Bloomberg U.S. Aggregate Bond Index	-10.3	-0.9	2.6
2	US Equity	S&P 500 Index	-10.6	19.6	24.6
3	Developed Equity	MSCI EAFE ND USD Ind	-17.8	18.8	11.5
4	Emerging Equity	MSCI EM (Emerging Markets) ND USD	-25.3	1.8	12.6
5	Technology	S&P 500 Information Technology Ind	-13.6	40.3	41.8
6	Natural Resources	Preqin Natural Resources Ind	22.2	3.1	8.1
7	Real Estate	Cambridge Associates Real Estate	19.3	-1.9	-4.1
8	Private Equity	Cambridge Associates U.S. Private Equity	6.1	6.2	6.3
9	Venture Capital	Cambridge Associates U.S. Venture Capital	2.1	-10.3	-2.1
10	Hedge Funds	HFRI Fund Weighted Composite Index	-5.6	5.0	10.0

*MPI Standard deviation estimates are based on the most recent estimated fund exposures (obtained through dynamic analysis of annual returns) and quarterly returns of risk factors over 10-year trailing estimation window.

The standard deviation estimates depicted in the diagram below are based on historical fund exposures and quarterly returns of risk factors over a 10-year trailing window. These exposures are obtained through dynamic analysis of annual returns using MPI Stylus Pro. The Y-axis on both charts shows the 10-year average annual returns (geometrically compounded).

The standard deviation estimates shown on the X-axis of the diagram below are derived from the last ten annual returns reported by each endowment.

*Sharpe Ratio calculation is based on a) 10-year reported annual returns and b) quarterly standard deviation estimates based on historical fund exposures (obtained through dynamic analysis of annual returns) and quarterly returns of risk factors over 10Y trailing estimation window.

Endowment Liquidity Analysis

This analysis of elite endowments’ liquidity-associated risks is based on schools’ latest annual financial statements.^[1] We focus on Private Equity(PE+VC) exposure as the main source of risk and stress due to its highly illiquid nature and its prominence in the “Yale Model” (25%-45% of the AUM for Ivys).^[2]

How to interpret the following diagram:

Generally speaking, the higher the portion of an endowment’s PE allocation that is unfunded (X-axis) -> the younger the average vintage of an endowment’s PE portfolio -> the lower the cash distributions from the LP’s PE portfolio likely are -> the less money the LP will have on hand that can be allocated to future capital calls from the GPs.^[3]
PE is one of the most illiquid assets in an endowment’s portfolio. The higher an endowment’s allocation to PE is (Y-axis), the greater the liquidity stress that could arise in a time of crisis.
“Ivy Average” could be used as a reference point for both stress and exposure to PE of individual endowments.
Size of the dots corresponds to assets.

The funding of capital calls typically comes from two sources: 1. The liquid portion of the endowment portfolio and 2. Distributions from the existing allocation to PE. Both sources of funding represent risks, which are illustrated as bars in the following diagram:

The higher the ratio of unfunded PE commitments to the liquid portion^[4] of an endowment, the greater the challenges for the endowment to meet capital calls.
The higher the unfunded portion of a PE allocation is, the lower the cash distributions will likely be coming from GPs that can be allocated towards future capital calls.

Liquidity risk and market risk should not be looked at in isolation as market drawdowns exacerbate liquidity pains. The following diagram compares endowments’ estimated market risk (X-axis)^[5] with liquidity risk defined as a ratio of unfunded PE commitments to the liquid portion^[4] of the endowment portfolio.

The actual dollar amounts of unfunded commitments add an extra layer to our liquidity analysis. Higher amounts of unfunded commitments could lead to exponentially greater liquidity stress, assuming all other factors remain constant.

This table contains source data for the charts above.^[6] Please note that sorting columns in the table affects the order of bar-charts.

Name	Endowment	MPI360 Report	AUM ($B)	Assets	Cash+Bonds, %	Equity, %	Hedge Funds, %	Private Equity, %	Real Assets, %	Liquid Assets, %	Illiquid Assets, %	Unfunded PE, % of PE Total (incl. Unfunded)	Unfunded PE, % of Endowment Assets	Unfunded PE, % of Liquid Assets	PE Alloc ($B)	PE Unfunded Commitments ($B)	PE Total (Allocated+Unfunded) ($B)	Market Risk, % (10Y estimated)
Brown	Brown	PDF	6.2	11.135529	14.0	15.0	21.0	40.0	11.0	27.9	72.1	34.3	21.9	78.3	2.86	1.49	4.4	24.2
Columbia	Columbia	PDF	13.6	16.492423	10.4	25.3	27.0	24.4	13.0	35.7	64.3	31.3	11.1	31.1	3.57	1.62	5.2	15.3
Cornell	Cornell	PDF	10.0	14.142136	18.0	18.7	19.2	29.1	15.0	36.7	63.3	19.2	6.9	18.8	3.06	0.73	3.8	18.6
Dartmouth	Dartmouth	PDF	7.9	12.569805	9.1	22.0	19.5	37.4	11.9	31.1	68.9	17.7	8.1	25.9	3.35	0.72	4.1	22.4
Harvard	Harvard	PDF	50.7	31.843367	10.5	15.0	29.4	39.3	5.8	25.3	74.7	27.9	15.3	60.3	23.12	8.96	32.1	20.6
MIT	MIT	PDF	24.7	22.226111	12.1	21.0	15.3	34.5	17.2	33.1	66.9	22.4	9.9	30.0	10.54	3.04	13.6	25.8
Princeton	Princeton	PDF	33.4	25.845696	5.8	18.0	23.4	39.9	12.9	21.6	81.2	21.3	11.3	52.3	14.12	3.82	17.9	23.2
Stanford	Stanford	PDF	35.5	26.645825	8.5	20.0	14.3	34.3	23.0	28.4	71.6	24.7	11.2	39.5	16.90	5.53	22.4	18.0
UPenn	Upenn	PDF	21.0	20.493902	11.1	23.8	18.9	34.7	11.5	34.9	65.1	29.8	14.7	42.1	7.85	3.33	11.2	19.8
Yale	Yale	PDF	40.7	28.530685	9.7	21.6	12.1	45.0	11.5	31.4	68.6	24.4	14.5	46.3	20.20	6.51	26.7	19.7
Ivy Average	Ivy Average		22.9	21.418450	10.5	20.1	21.7	36.7	11.4	30.6	69.8	25.7	13.0	44.4	9.77	3.40	13.2	19.7

[1] Endowment asset allocation numbers above were derived from 2023 endowment annual reports/financial statements except for Brown, Princeton and Harvard, which published endowment asset allocation information. Because financial statements cover the schools’ general accounts, which contain non-endowment assets, the computed allocations may differ slightly from actual. For Yale, in 2023 this is 7.2% of net investments, for Dartmouth 12.7%. Liabilities or other assets that are present in the annual reports and/or financial statements and can’t be classified are omitted from the charts due to uncertainty of their nature. For Yale, for example, in 2023 this represents 4.6% of net investments.
[2] Private Equity (PE) exposure includes also Venture Capital (VC) because most endowments report combined PE (buyout and growth) and VC allocations. On rare occasions where they are listed separately in financial statements (e.g., Brown and Yale), we combine them.
[3] Said differently, how funded or unfunded an endowment’s PE allocation is (X-axis) typically corresponds to average fund maturity/age, which in turn has a relationship to the likelihood of cash distributions from the PE portfolio. Such distributions can be allocated to future capital calls. Unfunded PE shown is a percentage of the total PE allocation and commitment (including the unfunded portion).
[4] For liquid assets we use the sum of Cash/Short-term, Bonds and Equity allocations. Not all of it could be available on daily, monthly or quarterly notice. The actual liquid assets could be significantly less than the stated number.
[5] For market risk we use annualized standard deviation estimates based on the most recent estimated fund exposures (obtained through dynamic analysis of endowments’ annual returns) and quarterly returns of risk factors over 10-year trailing estimation window
[6] Note on the source of a discrepancy in the Liquid Assets column on the data table where figures for “Equity” & “Cash + Bonds” for Harvard, Princeton and Brown don’t add up to 100%. Harvard and Brown reported endowment allocations. So, for the table’s columns of each asset class allocation, we used reported numbers. However, for the calculation of the liquid/illiquid assets we used data from financial statements primarily because we needed them to relate to unfunded liabilities in dollar terms.

MPI Stylus for Institutional Investors

Using quarterly or monthly data in your analyses, as opposed to annual, provides unparalleled precision as shown in this NORGES study

Disclaimer

Some statistics on this page are based on exposure estimates obtained through quantitative analysis and, beyond any public information, MPI does not claim to know or insinuate what the actual strategy, positions or holdings of the funds are, nor are we commenting on the quality or merits of the strategies. Deviations between our analysis and the actual holdings and/or management decisions made by funds are expected and inherent in any quantitative analysis. MPI makes no warranties or guarantees as to the accuracy of this statistical analysis, nor does it take any responsibility for investment decisions made by any parties based on this analysis.

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MPI Pension Tracker

Name	Pension	MPI360 Report	AUM($B)	2024 MPI Projected Return	2023 return	2022 return	2021 return	3Y return	5Y return	10Y return	Funded Ratio (GASB67)	StdDev 10Y	Sharpe Ratio 10Y	StdDev (current)	Sharpe Ratio (current)	Assets	Fiscal Year-end	3Q2022 Estimate	4Q2022 Estimate	1Q2023 Estimate	FY2023 Estimate	Ann StdDev	DW-stat	acoef	t_stat	p_value	Ljung-Box	Breusch-Godfrey	2Q2023 Estimate	Cash & Bonds	Public Equity	Private Equity	Absolute Return	Commodities	Real Assets
Arizona SRS	Arizona State Retirement System	PDF	49.8	9.1	8.2	1.1	25.1	11.0	8.0	8.5	0.75	11.5	0.66	12.5	0.73	9.9779	June	-4.7	5.1	3.7	6.8	8.3	1.3	0.3	1.5	0.1	0.1	0.1	2.8	6.3	39.0	36.2	0.0	0.0	18.5
Arkansas TRS	Arkansas Teacher Retirement System	PDF	19.7	11.4	8.7	-3.9	31.9	11.3	7.4	8.7	0.82	12.7	0.66	12.4	0.67	6.2737	June	-5.5	7.0	4.2	8.6	10.9	1.4	0.2	1.1	0.3	0.9	0.8	3.1	16.2	51.4	15.5	4.7	3.6	11.1
CalPERS	California Public Employees Retirement Fund	PDF	468.3	10.1	5.8	-6.1	21.3	6.4	6.1	7.1	0.73	9.8	0.70	9.9	0.74	30.6039	June	-5.3	5.8	4.9	8.7	8.1	1.4	0.3	1.4	0.2	0.0	0.0	3.4	28.0	42.0	15.0	0.0	0.0	15.0
CalSTRS	California State Teachers� Retirement System	PDF	315.6	10.7	6.3	-1.3	27.2	10.1	8.2	8.7	0.81	11.1	0.73	11.9	0.76	25.1237	June	-4.9	5.2	4.3	8.2	8.6	1.3	0.3	1.6	0.1	0.0	0.0	3.6	11.6	40.4	15.5	16.4	0.0	16.1
Connecticut SERS	Connecticut State Employees Retirement System	PDF	49.3	10.4	8.5	-7.6	24.4	7.6	6.1	7.0	0.46	10.4	0.63	10.5	0.68	9.9298	June	-5.5	7.0	4.8	9.4	9.0	1.5	0.2	1.0	0.3	0.6	0.5	3.2	22.8	42.3	12.2	6.8	0.0	15.9
Connecticut TRS	Connecticut State Teachers� Retirement System	PDF	49.3	10.3	8.5	-7.8	24.3	7.5	6.0	6.9	0.54	10.3	0.62	10.5	0.67	9.9298	June	-4.8	6.0	4.8	10.4	9.1	1.5	0.2	1.0	0.3	0.7	0.6	4.4	22.3	42.4	12.4	6.9	0.0	16.0
Florida RS	Florida Retirement System	PDF	185.7	12.5	7.5	-6.3	29.5	9.3	7.4	8.0	0.82	11.3	0.69	11.4	0.73	19.2717	June	-5.9	7.9	6.5	14.1	9.9	1.4	0.2	1.1	0.3	0.6	0.5	5.5	17.4	50.6	9.4	11.4	0.0	11.2
Georgia TRSGA	Teachers Retirement System of Georgia	PDF	95.0	16.3	11.9	-12.8	29.2	8.0	7.3	7.9	0.76	11.0	0.70	11.1	0.74	13.7834	June	-5.2	5.0	3.0	4.5	10.9	1.6	0.2	0.8	0.4	1.8	1.6	1.9	30.6	69.4	0.0	0.0	0.0	0.0
Hawaii ERS	Employees� Retirement System of the State of Hawaii	PDF	22.3	6.9	2.6	4.0	26.6	10.6	7.7	8.0	0.62	11.2	0.65	12.2	0.71	6.6783	June	-5.9	7.2	5.6	11.2	8.6	1.2	0.4	1.7	0.1	0.1	0.1	4.4	15.7	25.8	0.0	42.4	0.0	16.2
Idaho PERS	Public Employee Retirement System of Idaho	PDF	22.4	13.6	9.6	-9.5	27.9	8.3	7.2	7.9	0.84	11.3	0.64	11.6	0.71	6.6933	June	-4.8	6.2	5.0	10.7	10.0	1.4	0.2	1.1	0.3	0.9	0.7	4.3	29.1	54.0	7.6	0.0	0.0	9.3
Illinois SERS	State Employees' Retirement System of Illinois	PDF	22.2	11.3	6.2	-6.3	25.8	7.8	7.0	7.6	0.41	10.1	0.73	10.0	0.73	6.6670	June	-5.0	5.8	3.7	6.8	9.1	1.4	0.3	1.3	0.2	0.1	0.1	2.5	28.0	43.0	9.0	6.0	2.0	14.0
Illinois TRS	Teachers' Retirement System of The State of Illinois	PDF	66.5	9.4	7.2	-1.2	25.5	10.0	7.1	7.7	0.44	10.7	0.66	11.4	0.68	11.5330	June	-5.0	5.1	3.8	6.3	8.5	1.4	0.3	1.3	0.2	0.1	0.1	2.6	24.6	34.7	17.0	5.1	2.7	18.6
Indiana PERF	State of Indiana Public Employees' Retirement Fund	PDF	39.6	10.0	2.2	-6.6	25.5	6.2	5.7	6.0	0.81	9.9	0.58	9.7	0.62	8.8994	June	-5.8	5.9	4.6	8.3	8.8	1.5	0.2	1.0	0.3	0.1	0.1	3.7	29.1	17.8	14.3	23.6	8.1	15.2
Iowa PERS	Iowa Public Employees' Retirement System	PDF	41.1	11.8	5.4	-3.9	29.6	9.5	8.0	8.1	0.90	11.4	0.67	12.4	0.72	9.0664	June	-5.4	6.1	4.5	8.6	9.2	1.3	0.3	1.6	0.1	0.5	0.5	3.5	23.0	42.0	19.0	5.0	3.0	11.0
Kansas PERS	Kansas Public Employees Retirement System	PDF	25.6	11.5	7.4	-4.7	26.3	8.9	7.1	7.9	0.71	11.0	0.70	11.2	0.71	7.1554	June	-4.8	6.4	5.8	13.4	9.0	1.3	0.3	1.4	0.2	0.3	0.2	5.8	19.4	47.7	0.0	0.0	10.4	22.0
Kentucky TRS	Teachers� Retirement System of the State of Kentucky	PDF	23.9	16.1	10.6	-10.7	29.9	8.7	7.5	8.5	0.58	11.3	0.75	10.7	0.76	6.9138	June	-5.0	5.2	3.6	6.1	11.0	1.5	0.2	1.0	0.3	1.1	1.0	2.5	22.2	60.4	8.0	0.0	2.0	9.4
LA County ERS	Los Angeles County Employees Retirement Association	PDF	73.6	9.1	6.4	0.1	25.2	10.1	7.6	8.1	0.83	10.8	0.66	11.9	0.72	12.1326	June	-4.9	6.5	5.1	11.6	7.9	1.2	0.4	1.7	0.1	0.2	0.2	5.0	15.0	33.4	18.3	17.5	8.3	15.3
Los Angeles ERS	Los Angeles City Employees' Retirement System	PDF	21.6	11.7	6.8	-6.9	29.3	8.7	6.7	7.6	0.71	11.5	0.67	11.1	0.65	6.5727	June	-5.7	7.9	5.8	13.7	10.3	1.4	0.2	1.1	0.3	0.4	0.3	5.5	14.6	45.3	18.9	8.5	12.8	12.8
Louisiana TRSL	Louisiana State Teachers Retirement System	PDF	25.2	13.5	6.8	-7.3	36.4	10.5	7.9	9.1	0.74	13.5	0.68	12.5	0.67	7.0993	June	-4.1	4.2	3.6	7.4	12.3	1.5	0.2	1.0	0.3	0.5	0.5	3.7	14.6	37.7	0.0	0.0	0.0	12.0
Maryland PERS	Maryland State Retirement and Pension System - Employees Combined System	PDF	65.2	10.5	3.1	-3.0	26.7	8.2	6.9	7.0	0.70	9.7	0.70	10.3	0.70	11.4193	June	-4.1	4.2	3.6	7.4	8.4	1.4	0.3	1.3	0.2	0.1	0.1	3.7	18.6	29.4	21.9	14.9	4.7	15.2
Maryland TRPS	Maryland State Retirement and Pension System - Teachers Combined System	PDF	65.2	10.5	3.1	-3.0	26.7	8.2	6.9	7.0	0.77	9.7	0.70	10.3	0.70	11.4193	June	-4.9	5.7	4.3	9.0	8.4	1.4	0.3	1.3	0.2	0.1	0.1	4.0	18.6	29.4	21.9	14.9	4.7	15.2
Massachusetts SERS	Massachusetts State Retirement System	PDF	96.6	11.2	6.0	-3.0	30.0	10.2	7.8	8.5	0.73	11.4	0.70	11.7	0.72	13.8996	June	-4.9	5.7	4.3	9.0	9.5	1.3	0.3	1.5	0.1	0.1	0.1	4.0	21.3	37.3	18.4	8.7	3.2	13.8
Massachusetts TRS	Massachusetts Teachers� Retirement System	PDF	96.6	11.2	6.0	-3.0	30.0	10.2	7.8	8.5	0.59	11.4	0.70	11.7	0.72	13.8996	June	-5.0	5.9	5.1	10.9	9.5	1.3	0.3	1.5	0.1	0.1	0.1	4.9	21.3	37.3	18.4	8.7	3.2	13.8
Minnesota GERF	Minnesota General Employees Retirement Plan	PDF	27.5	14.4	8.9	-6.4	30.3	9.9	8.2	8.8	0.83	12.0	0.72	12.1	0.76	7.4163	June	-5.0	5.9	5.1	10.9	10.3	1.3	0.3	1.4	0.2	0.4	0.4	4.9	22.0	52.0	26.0	0.0	0.0	0.0
Minnesota SRS	Minnesota State Employees Retirement Fund	PDF	85.7	14.8	8.9	-6.4	30.3	9.9	8.2	8.8	0.95	11.9	0.73	12.0	0.76	13.0920	June	-5.2	7.2	5.8	13.5	10.3	1.3	0.3	1.4	0.2	0.4	0.4	5.6	24.2	50.6	0.0	0.0	0.0	0.0
Minnesota TRA	Teachers Retirement Association of Minnesota	PDF	26.8	14.8	8.9	-6.4	30.3	9.9	8.2	8.8	0.76	11.9	0.73	12.0	0.76	7.3150	June	-4.6	5.3	4.5	9.3	10.3	1.3	0.3	1.4	0.2	0.4	0.4	4.2	24.6	50.4	0.0	0.0	0.0	0.0
Mississippi PERS	Public Employees� Retirement System of Mississippi	PDF	31.2	13.6	7.8	-8.5	32.7	9.4	7.6	8.5	0.56	12.2	0.69	11.8	0.72	7.8994	June	-5.0	5.4	5.2	10.7	11.2	1.5	0.2	1.0	0.3	0.6	0.5	5.0	19.6	58.5	12.0	0.0	0.0	9.9
Missouri PSRS	Public School Retirement System of Missouri	PDF	48.8	11.6	6.2	-2.8	28.7	9.9	8.1	8.5	0.85	11.0	0.72	11.7	0.76	9.8826	June	-5.1	6.5	4.7	10.1	8.9	1.3	0.3	1.5	0.1	0.2	0.2	3.9	19.3	38.1	22.5	9.1	0.0	11.0
Nevada PERS	Public Employees� Retirement System of Nevada - Regular Employees Plan	PDF	58.0	14.6	9.1	-5.1	27.3	9.6	8.9	8.9	0.76	10.7	0.78	11.7	0.80	10.7703	June	-5.1	6.5	4.7	10.1	8.7	1.2	0.4	1.7	0.1	0.3	0.3	3.9	25.7	60.7	0.0	0.0	0.0	0.0
New Jersey PERS	Public Employees� Retirement System of New Jersey	PDF	91.4	13.0	9.1	-7.9	28.6	8.9	6.8	7.5	0.46	11.1	0.65	11.0	0.69	13.5204	June	-5.1	6.5	4.7	10.1	10.2	1.5	0.2	1.0	0.3	1.5	1.3	3.9	23.8	42.3	12.6	10.5	2.6	9.0
New York City ERS	New York City Employees' Retirement System	PDF	253.2	12.4	8.0	-8.4	26.6	7.8	6.8	7.7	0.82	10.9	0.68	10.9	0.72	22.5033	June	-5.4	6.7	5.4	11.1	9.5	1.4	0.2	1.1	0.3	0.6	0.5	4.4	34.8	45.0	12.9	0.0	0.0	7.3
New York City PRS	New York Police Pension Fund Article 2	PDF	49.6	13.6	8.6	-6.7	26.7	8.7	7.6	8.4	0.86	10.9	0.73	11.1	0.76	9.9608	June	-5.2	5.9	5.2	10.5	9.2	1.3	0.3	1.4	0.2	0.4	0.3	4.6	32.7	42.9	9.1	5.4	2.3	9.2
New York City TRS	Teachers� Retirement System of the City of New York	PDF	67.9	13.3	8.2	-9.4	25.1	7.1	6.8	7.7	0.83	10.5	0.71	10.5	0.75	11.6563	June	-5.4	6.5	5.7	11.5	9.3	1.4	0.3	1.2	0.2	0.4	0.3	4.8	28.7	54.0	17.3	0.0	0.0	0.0
New York STRS	New York State Teachers� Retirement System	PDF	137.2	13.0	9.0	-7.1	29.0	9.3	7.7	8.5	0.99	11.2	0.72	11.3	0.75	16.5663	June	-4.8	6.7	5.4	12.3	9.7	1.4	0.3	1.3	0.2	0.2	0.2	4.9	23.4	51.5	12.9	0.0	0.0	12.2
North Carolina LGERS	North Carolina Local Governmental Employees� Retirement System	PDF	114.6	9.0	5.3	-7.3	19.1	5.2	5.3	6.3	0.82	9.1	0.64	9.2	0.70	15.1394	June	-5.4	6.0	4.8	8.2	7.5	1.3	0.3	1.4	0.2	0.4	0.3	2.9	43.9	36.4	5.4	6.8	0.0	7.5
North Carolina TSERS	Teachers� and State Employees� Retirement System of North Carolina	PDF	114.6	9.0	5.3	-7.3	19.1	5.2	5.3	6.3	0.83	9.1	0.64	9.2	0.70	15.1394	June	-5.4	6.0	4.8	8.2	7.5	1.3	0.3	1.4	0.2	0.4	0.3	2.9	43.9	36.4	5.4	6.8	0.0	7.5
Ohio STRS	Ohio State Teachers Retirement System	PDF	90.1	12.5	7.6	-3.7	29.2	10.2	8.1	8.7	0.80	11.4	0.73	11.8	0.75	13.4239	June	-4.7	5.5	4.5	9.4	9.3	1.3	0.3	1.5	0.1	0.1	0.1	4.2	21.5	47.9	0.0	0.0	0.0	9.8
Oklahoma TRS	Teachers' Retirement System of Oklahoma	PDF	20.7	12.7	8.0	-9.6	33.0	9.1	6.6	8.0	0.73	12.7	0.67	11.3	0.67	6.4343	June	-5.1	7.9	5.6	13.5	12.3	1.5	0.2	1.0	0.3	0.8	0.7	5.0	25.0	58.9	7.3	1.0	0.0	7.8
Oregon PERS	Oregon Public Employees Retirement System	PDF	83.5	7.7	3.2	6.3	25.5	11.3	8.1	8.3	0.82	11.0	0.69	12.3	0.72	12.9219	June	-4.7	3.2	2.0	1.8	7.7	1.2	0.4	1.9	0.1	0.0	0.0	1.4	20.0	23.3	26.5	7.5	9.1	22.7
Pennsylvania PSERS	Public School Employees� Retirement System of Pennsylvania	PDF	72.1	8.2	3.5	2.2	24.6	9.7	7.3	7.5	0.62	10.3	0.63	11.2	0.71	12.0094	June	-5.0	3.7	2.6	2.9	7.5	1.2	0.3	1.6	0.1	0.0	0.0	1.9	30.7	26.0	17.6	6.8	9.1	18.9
San Francisco SFERS	San Francisco City & County Employees' Retirement System	PDF	32.8	11.1	4.3	-2.9	33.7	10.6	8.4	9.0	0.92	13.1	0.67	13.2	0.71	8.0992	June	-5.8	6.7	5.0	10.1	10.7	1.3	0.3	1.4	0.1	0.2	0.2	4.3	5.2	29.9	32.6	17.2	0.0	15.1
South Carolina RS	South Carolina Retirement System	PDF	40.6	8.4	6.8	-1.0	28.6	10.8	7.2	7.1	0.59	11.5	0.55	12.5	0.66	9.0111	June	-5.5	6.2	3.3	5.5	9.3	1.7	0.2	0.7	0.5	1.0	0.9	1.8	11.2	42.9	22.5	12.2	0.0	11.2
Tennessee CRS	Tennessee Political Subdivisions Retirement Plan	PDF	65.2	11.4	6.7	-3.6	25.6	8.9	7.8	8.1	0.98	10.3	0.71	11.2	0.78	11.4193	June	-5.0	4.8	3.6	6.5	8.1	1.2	0.4	1.7	0.1	0.3	0.3	3.2	21.2	42.2	23.7	0.0	0.0	13.0
Texas TRS	Teacher Retirement System of Texas	PDF	184.2	10.0	4.0	-2.3	27.3	9.0	7.0	7.8	0.76	10.9	0.68	11.1	0.69	19.1930	June	-4.9	5.3	4.7	9.2	8.8	1.4	0.3	1.3	0.2	0.3	0.3	4.1	14.0	34.0	16.2	14.4	6.0	21.4
Univ of California UCRP	University of California Retirement Plan	PDF	88.3	14.3	10.1	-10.8	30.5	8.6	6.7	7.5	0.81	11.4	0.64	11.2	0.66	13.2863	June	-5.6	7.5	5.3	11.6	11.3	1.7	0.1	0.5	0.6	1.3	1.1	4.5	13.0	50.0	11.0	0.0	16.0	26.0
Virginia RS	Virginia Retirement System	PDF	104.9	9.4	6.1	0.6	27.5	10.8	8.1	8.2	0.85	10.9	0.67	12.2	0.73	14.4845	June	-4.9	4.9	3.3	5.7	8.2	1.3	0.3	1.6	0.1	0.1	0.1	2.6	15.6	33.0	18.2	19.7	0.0	13.5
Washington PERS 2	Washington Public Employees� Retirement System	PDF	155.7	10.2	5.2	5.4	28.7	12.6	9.9	9.7	1.07	12.0	0.71	13.9	0.77	17.6465	June	-5.1	3.8	3.2	4.5	8.0	1.1	0.4	2.1	0.0	0.0	0.0	2.7	16.3	25.8	28.8	1.1	6.9	27.9
60-40 Global Bmk	60-40 Global Benchmark			12.9	9.4	-13.4	22.3	5.0	5.6	6.1		9.6	0.60	9.6	0.60	5.0000	June	-6.0	6.6	5.6	9.4	9.5	1.6	0.2	0.8	0.4	0.9	0.8	3.4
Pension Avg	Pension Avg		84.8	11.3	6.9	-4.7	27.7	9.2	7.4	8.0	0.75	11.2	0.67	11.6	0.72	13.0219	June					9.3	1.4	0.3	1.3	0.2	0.3	0.3

*As of June 2023; 3-year, 5-year and 10-year returns are annualized. Standard deviation estimates are based on either historical or the most recent fund exposures (obtained through dynamic analysis of annual returns) and quarterly returns of risk factors over 10-year trailing window. 60-40 Global Benchmark is a quarterly rebalanced portfolio of 60% MSCI ACWI Index and 40% Bloomberg Barclay Aggregate Bond Index.
06/24/2024: All pension charts and tables updated with 2023 Fiscal Year data.

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*Standard deviation estimates are based on the most recent estimated fund exposures (obtained through dynamic analysis of annual returns) and quarterly returns of risk factors over 10-year trailing estimation window.

*Standard deviation estimates are based on historical fund exposures (obtained through dynamic analysis of annual returns) and quarterly returns of risk factors over 10-year trailing window.

Underestimation of Pension Risk

Autocorrelation in fund returns frequently leads to underestimation of market risk. In many cases such autocorrelation is caused by allocations to illiquid assets with stale NAVs such as private equity, private credit and real assets. To identify pensions with such issues we compute Durbin-Watson (DW) statistics of their annual returns (lower DW values imply more significant autocorrelation). We provide additional tests for autocorrelation in the table below for statistically inclined.

*Standard deviation estimates and Durbin-Watson statistic are based on trailing 10 years of public annual returns (through June 2023). On a 5% significance level, DW<0.9 indicates strong autocorrelation, 0.9<DW<1.3 – potential autocorrelation and DW>1.3 – no positive autocorrelation. Please see DW Statistical Tables for details. More on Durbin-Watson and liquidity. Note that quarterly returns may exhibit much higher levels of autocorrelation.

The above chart compares volatility estimates using pension reported annual returns (X-axis) with the MPI approach based on quarterly returns of a factor-model proxy portfolio (Y-axis)

Autocorrelation Statistics
(first order autocorrelation tests)

Name	Pension	MPI360 Report	AUM($B)	2022 return	2021 return	3Y return	5Y return	10Y return	Private Equity (%, reported)	Funded Ratio (GASB67)	StdDev 10Y Corrected	Sharpe Ratio 10Y Corrected	StdDev (current)	Sharpe Ratio (current)	Assets	Fiscal Year-end	3Q2022 Estimate	4Q2022 Estimate	1Q2023 Estimate	FY2023 Estimate	10Y StdDev Original	DW-stat	Autocorrelation ACF(1)	ACF t-stat	ACF p-value	Ljung-Box	Breusch-Godfrey
Arizona SRS	Arizona State Retirement System	PDF	47.1	1.1	25.1	8.4	8.3	9.0	36	0.74	11.3	0.67	11.3	0.72	9.7046	June	-4.7	5.1	3.7	6.8	8.4	1.13	0.38	1.31	0.19	2.07	1.76
Arkansas TRS	Arkansas Teacher Retirement System	PDF	21.5	-3.9	31.9	7.7	8.0	9.3	13	0.89	12.7	0.64	12.6	0.63	6.5527	June	-5.5	7.0	4.2	8.6	11.0	1.47	0.21	0.69	0.49	2.94	2.71
CalPERS	California Public Employees Retirement Fund	PDF	439.4	-6.1	21.3	6.1	6.7	7.8	12	0.82	9.9	0.70	10.0	0.74	29.6432	June	-5.3	5.8	4.9	8.7	8.3	1.13	0.36	1.25	0.21	1.58	1.84
CalSTRS	California State Teachers Retirement System	PDF	300.1	-1.3	27.2	9.3	8.7	9.5	16	0.81	11.2	0.73	11.7	0.75	24.4972	June	-4.9	5.2	4.3	8.2	8.7	1.08	0.40	1.41	0.16	2.23	2.09
Connecticut TRS	Connecticut State Teachers Retirement System	PDF	21.6	-7.8	24.3	5.3	5.8	7.3	12	0.61	10.4	0.62	10.6	0.65	6.5650	June	-5.5	7.0	4.8	9.4	9.2	1.49	0.19	0.60	0.55	3.09	3.53
Florida RS	Florida Retirement System	PDF	180.2	-6.3	29.5	7.7	7.7	8.6	10	0.83	11.3	0.70	11.6	0.73	18.9856	June	-4.8	6.0	4.8	10.4	10.0	1.40	0.24	0.78	0.43	2.91	3.05
Georgia TRSGA	Teachers Retirement System of Georgia	PDF	87.1	-12.8	29.2	5.9	6.7	8.1	0	0.73	11.3	0.69	12.1	0.73	13.2002	June	-5.9	7.9	6.5	14.1	11.0	1.47	0.19	0.57	0.57	2.78	3.88
Hawaii ERS	Employees Retirement System of the State of Hawaii	PDF	21.9	3.7	26.6	10.0	8.7	9.0	14	0.64	11.1	0.66	11.7	0.67	6.6236	June	-5.2	5.0	3.0	4.5	8.4	1.11	0.39	1.35	0.18	1.58	1.25
Idaho PERS	Public Employee Retirement System of Idaho	PDF	19.4	-9.5	27.9	6.1	7.1	7.8	8	0.83	11.3	0.64	11.8	0.71	6.2232	June	-5.9	7.2	5.6	11.2	10.0	1.55	0.18	0.55	0.58	3.86	4.71
Illinois SERS	State Employees' Retirement System of Illinois	PDF	22.2	-6.3	25.8	7.2	7.3	8.4	9	0.41	10.3	0.72	10.9	0.75	6.6670	June	-4.8	6.2	5.0	10.7	9.3	1.24	0.31	1.04	0.30	2.09	2.31
Illinois TRS	Teachers' Retirement System of The State of Illinois	PDF	62.8	-1.2	25.5	7.7	7.3	8.2	16	0.43	10.6	0.67	10.7	0.66	11.2101	June	-5.0	5.8	3.7	6.8	8.6	1.26	0.31	1.05	0.29	2.24	1.99
Illinois Universities	State Universities Retirement System of Illinois	PDF	22.5	-1.4	23.8	7.8	7.5	8.3	14	0.44	10.5	0.67	10.7	0.71	6.7117	June	-5.0	5.1	3.8	6.3	8.2	1.16	0.36	1.25	0.21	1.93	1.77
Iowa PERS	Iowa Public Employees' Retirement System	PDF	40.2	-3.9	29.6	8.8	8.5	8.6	21	0.91	11.5	0.67	12.3	0.72	8.9657	June	-5.8	5.9	4.6	8.3	9.2	1.37	0.28	0.90	0.37	3.58	3.54
Kansas PERS	Kansas Public Employees Retirement System	PDF	24.5	-4.7	26.3	7.1	7.4	8.5	0	0.70	11.0	0.69	11.1	0.70	6.9960	June	-5.4	6.1	4.5	8.6	9.2	1.28	0.30	0.99	0.32	2.48	2.53
Kentucky TRS	Teachers Retirement System of the State of Kentucky	PDF	22.9	-10.9	29.9	7.0	7.5	8.8	8	0.56	11.6	0.74	11.6	0.78	6.7676	June	-4.8	6.4	5.8	13.4	11.2	1.40	0.23	0.74	0.46	2.52	3.16
LA County ERS	Los Angeles County Employees Retirement Association	PDF	70.3	0.1	25.2	8.5	8.2	8.6	18	0.84	10.7	0.67	11.0	0.70	11.8566	June	-5.0	5.2	3.6	6.1	8.0	1.14	0.38	1.30	0.19	2.50	2.20
Los Angeles FPPS	Los Angeles City Fire and Police Pension System	PDF	25.3	-7.2	32.6	8.2	8.0	8.8	17	0.97	11.7	0.71	12.2	0.73	7.1075	June	-4.9	6.5	5.1	11.6	10.9	1.49	0.20	0.64	0.52	3.09	3.23
Louisiana TRSL	Louisiana State Teachers Retirement System	PDF	25.1	-7.7	36.4	8.5	8.8	9.8	0	0.72	13.6	0.68	13.7	0.71	7.0783	June	-5.7	7.9	5.8	13.7	12.4	1.55	0.18	0.56	0.57	3.39	3.44
Maryland PERS	Maryland State Retirement and Pension System - Employees Combined System	PDF	21.4	-3.0	26.7	8.4	7.9	7.8	22	0.72	9.8	0.71	10.4	0.76	6.5466	June	-4.1	4.2	3.6	7.4	8.3	1.31	0.30	0.99	0.32	2.67	2.55
Maryland TRPS	Maryland State Retirement and Pension System - Teachers Combined System	PDF	39.1	-3.0	26.3	8.3	7.9	7.8	22	0.79	9.7	0.71	10.3	0.76	8.8460	June	-4.1	4.2	3.6	7.4	8.2	1.30	0.30	1.01	0.31	2.68	2.59
Massachusetts SERS	Massachusetts State Retirement System	PDF	34.1	-3.0	30.0	8.9	8.6	9.2	18	0.78	11.5	0.70	11.9	0.73	8.2621	June	-4.9	5.7	4.3	9.0	9.5	1.29	0.30	1.01	0.31	2.98	2.84
Massachusetts TRS	Massachusetts Teachers Retirement System	PDF	35.4	-3.0	30.0	8.9	8.6	9.2	18	0.62	11.5	0.70	11.9	0.73	8.4125	June	-4.9	5.7	4.3	9.0	9.5	1.29	0.30	1.01	0.31	2.98	2.84
Minnesota GERF	Minnesota General Employees Retirement Plan	PDF	28.6	-6.4	30.3	8.3	8.5	9.4	8	0.87	12.0	0.73	12.1	0.76	7.5614	June	-5.0	5.9	5.1	10.9	10.4	1.32	0.29	0.94	0.35	2.72	2.90
Minnesota TRA	Teachers Retirement Association of Minnesota	PDF	25.6	-6.4	30.3	8.3	8.5	9.4	0	0.76	12.0	0.73	12.1	0.76	7.1549	June	-5.0	5.9	5.1	10.9	10.4	1.32	0.29	0.94	0.35	2.72	2.90
Mississippi PERS	Public Employees Retirement System of Mississippi	PDF	30.8	-8.5	32.7	7.9	8.0	9.0	13	0.60	12.3	0.69	12.8	0.74	7.8474	June	-5.2	7.2	5.8	13.5	11.3	1.49	0.20	0.63	0.53	3.21	3.51
Missouri PSRS	Public School Retirement System of Missouri	PDF	47.7	-3.0	28.7	9.1	8.6	9.1	23	0.86	11.1	0.72	11.7	0.77	9.7643	June	-4.6	5.3	4.5	9.3	9.0	1.21	0.34	1.16	0.25	2.82	2.76
Nevada PERS	Public Employees Retirement System of Nevada - Regular Employees Plan	PDF	41.9	-5.1	27.3	9.0	8.8	9.2	0	0.75	10.8	0.77	11.8	0.81	9.1588	June	-5.0	5.4	5.2	10.7	8.8	1.11	0.40	1.36	0.17	2.44	2.76
New Jersey PERS	Public Employees Retirement System of New Jersey	PDF	32.6	-7.9	28.6	6.2	6.8	7.8	13	0.46	11.1	0.65	11.1	0.68	8.0707	June	-5.1	6.5	4.7	10.1	10.2	1.57	0.16	0.49	0.62	3.29	3.54
New Jersey Police and Fire	The Police and Firemens Retirement System of New Jersey	PDF	30.7	-7.9	28.6	6.2	6.8	7.8	13	0.63	11.1	0.65	11.1	0.68	7.8369	June	-5.1	6.5	4.7	10.1	10.2	1.57	0.16	0.49	0.62	3.29	3.54
New Jersey Teachers	Teachers Pension and Annuity Fund of New Jersey	PDF	24.6	-7.9	28.6	6.2	6.8	7.8	13	0.32	11.1	0.65	11.1	0.68	7.0200	June	-5.1	6.5	4.7	10.1	10.2	1.57	0.16	0.49	0.62	3.29	3.54
New York City ERS	New York City Employees' Retirement System	PDF	85.9	-8.7	26.6	6.2	6.9	8.0	8	0.93	10.9	0.68	11.3	0.73	13.1077	June	-5.4	6.7	5.4	11.1	9.6	1.40	0.24	0.75	0.45	3.07	3.75
New York City Police	New York Police Pension Fund Article 2	PDF	47.0	-6.7	26.7	7.4	7.9	8.8	10	0.96	11.1	0.72	11.4	0.77	9.6998	June	-5.2	5.9	5.2	10.5	9.2	1.24	0.33	1.08	0.28	2.86	3.34
New York City TRS	Teachers Retirement System of the City of New York	PDF	64.0	-9.4	25.1	6.0	6.8	8.1	17	0.81	10.6	0.71	11.0	0.76	11.3145	June	-5.4	6.5	5.7	11.5	9.4	1.32	0.28	0.89	0.37	2.76	3.68
New York STRS	New York State Teachers Retirement System	PDF	132.0	-7.1	29.0	7.4	7.7	9.0	12	0.99	11.3	0.73	11.7	0.76	16.2459	June	-4.8	6.7	5.4	12.3	9.8	1.31	0.28	0.93	0.35	3.05	3.48
North Carolina LGERS	North Carolina Local Governmental Employees Retirement System	PDF	29.9	-7.3	19.1	4.8	5.7	6.7	6	0.84	9.1	0.65	9.3	0.71	7.7378	June	-5.4	6.0	4.8	8.2	7.6	1.26	0.31	1.01	0.31	2.32	3.05
North Carolina TSERS	Teachers and State Employees Retirement System of North Carolina	PDF	78.7	-7.3	19.1	4.8	5.7	6.7	6	0.84	9.1	0.65	9.3	0.71	12.5483	June	-5.4	6.0	4.8	8.2	7.6	1.26	0.31	1.01	0.31	2.32	3.05
Ohio STRS	School Employees Retirement System of Ohio	PDF	80.9	-3.7	29.2	8.7	8.5	9.3	0	0.79	11.5	0.72	11.8	0.75	12.7170	June	-4.7	5.5	4.5	9.4	9.4	1.24	0.32	1.09	0.28	3.02	3.01
Oklahoma TRS	Teachers' Retirement System of Oklahoma	PDF	22.1	-9.5	33.0	6.6	7.0	9.0	9	0.81	12.7	0.68	12.5	0.69	6.6411	June	-5.1	7.9	5.6	13.5	12.7	1.53	0.16	0.50	0.62	2.17	2.25
Oregon PERS	Oregon Public Employees Retirement System	PDF	83.8	6.3	25.5	10.3	9.4	9.3	26	0.85	10.9	0.70	11.0	0.69	12.9437	June	-4.7	3.2	2.0	1.8	7.6	0.94	0.47	1.71	0.09	1.26	0.97
Pennsylvania PSERS	Public School Employees Retirement System of Pennsylvania	PDF	70.5	2.3	24.6	8.8	8.5	7.9	16	0.61	10.3	0.63	10.5	0.68	11.8767	June	-5.0	3.7	2.6	2.9	7.3	1.19	0.38	1.26	0.21	2.68	2.18
San Francisco SFERS	San Francisco City & County Employees' Retirement System	PDF	32.8	-2.8	33.7	10.0	9.9	9.9	33	0.92	13.2	0.67	13.6	0.72	8.0992	June	-5.8	6.7	5.0	10.1	10.5	1.34	0.29	0.94	0.35	3.07	2.84
South Carolina RS	South Carolina Retirement System	PDF	32.2	-0.9	28.6	7.8	7.4	7.5	24	0.57	11.5	0.55	11.9	0.60	8.0265	June	-5.5	6.2	3.3	5.5	9.3	1.60	0.17	0.52	0.60	2.98	2.50
Texas TRS	Teacher Retirement System of Texas	PDF	184.2	-2.3	27.3	8.2	8.0	8.4	16	0.76	10.8	0.68	11.1	0.70	19.1930	June	-5.0	4.8	3.6	6.5	8.7	1.32	0.30	0.99	0.32	3.35	3.13
Tennessee STRP	Tennesee State and Teachers' Retirement Plan	PDF	31.7	-3.6	25.6	8.3	8.1	8.4	18	1.22	10.4	0.71	11.2	0.78	7.9575	June	-4.9	5.3	4.7	9.2	8.1	1.19	0.37	1.23	0.22	3.13	3.27
Univ of California UCRP	University of California Retirement Plan	PDF	81.4	-10.8	30.5	5.8	6.2	7.6	11	0.79	11.5	0.63	11.8	0.66	12.7615	June	-5.6	7.5	5.3	11.6	11.4	1.71	0.08	0.24	0.81	3.37	3.83
Virginia RS	Virginia Retirement System	PDF	97.4	0.6	27.5	9.2	8.3	8.7	19	0.85	10.7	0.69	11.3	0.69	13.9546	June	-4.9	4.9	3.3	5.7	8.2	1.20	0.35	1.19	0.23	2.84	2.44
Washington PERS 23	Washington Public Employees Retirement System	PDF	58.9	5.4	28.7	12.0	10.9	10.4	29	1.07	12.0	0.71	12.7	0.76	10.8487	June	-5.1	3.8	3.2	4.5	7.9	0.88	0.52	1.90	0.06	1.85	1.47
60-40 Global Bmk	60-40 Global Benchmark		0.0	-13.4	22.3	3.9	5.0	6.1			9.6	0.60	9.6	0.60	5.0000	June	-6.0	6.6	5.6	9.4	9.5	1.54	0.14	0.42	0.67	2.28	3.67

Our estimates of FY2024 pension returns are gross of most management fees and operational expenses. The estimates are projected based on FY2023 factor exposures (obtained through MPI’s dynamic analysis of annual returns and rebalanced quarterly) and do not account for any potential portfolio adjustments nor asset class reallocations made during FY2024.

Annual return data sources:

Public Plans Data. Center for Retirement Research at Boston College, MissionSquare Research Institute, National Association of State Retirement Administrators, and the Government Finance Officers Association.
Pension funds’ annual reports.

MPI Stylus for Institutional Investors

Using quarterly or monthly data in your analyses, as opposed to annual, provides unparalleled precision as shown in this NORGES study

Disclaimer

The detailed individual PDF reports are free with registration

On Demand Endowment Reports

Name	Endowment	MPI360 Report	AUM ($B)	2022 return	MPI Analyst Commentary
Rutgers University	Rutgers	PDF	1.8	-9.7	FY2022: A Tough Year for Rutgers
Duke University	Duke	PDF	12.1	-1.5	Coming soon
Oklahoma State University	Oklahoma State	PDF	0.8	-1.9	Coming soon
University of Chicago	UChicago	PDF	10.3	-8.8	University of Chicago's Endowment FY2022 Risk Premia Story

We are constantly adding to our list of available reports. Would you like us to cover a specific endowment/pension?

Disclaimer

For 25 years, MPI’s clients have used our tools to gain insight into some of the largest and most sophisticated investment products. Now, for the first time, MPI is making available free, proprietary analyses and reports on specific endowments and pensions to help everyone understand most fundamental potential drivers of their investment behavior.

Driven by our MPI Stylus Pro technology, these MPI-360 transparency reports shine a light on portfolio return, risk, and effective asset-class exposures in a way unavailable anywhere else. Whether you are an institutional investor, asset manager, wealth advisor, researcher, or journalist, you can read below to learn why we believe transparency into these impactful portfolios is important and how we go about pulling back the curtains on their behavior.

And, of course, you can contact us to speak with MPI and learn why MPI is the provider of choice for anyone seeking to understand complex fund and portfolio behavior for which limited data is available.

1. Why Transparency?
2. Why MPI?
3. Methodology
4. MPI-360 Reports
5. FAQ

1. Why Transparency?

College and charitable endowments, pension funds, hedge funds, sovereign wealth funds…even as frustrated stakeholders call for greater transparency, the world’s largest and most important investments tend to remain shrouded in mystery. Yet the larger these funds get, the more they may not only be affected by markets, but also exert an influence on markets, both public and private. It’s fair to wonder what exactly is inside these tax-sheltered or tax-advantaged investing machines.

MPI remains committed to helping investment professionals, stakeholders, and the public form a clearer picture of these investment giants and their behavior through our endowment transparency and pension transparency reports.

Endowment Transparency

College endowments are famously opaque, providing so little information that their once-a-fiscal-year reports have become a regular autumn spectacle. But seldom do these reports contain much real information about how endowments achieve results, how they decide which risks to take, or how outsiders can independently verify their results.

Disclosures from endowments are minimal, typically consisting of annual performance figures, updates to policy portfolios and general commentary. Endowments often have highly restrictive media policies. Even many institutional consultants struggle to explain top-tier endowment results. Although much has been written on the endowment model for investing by gurus like Yale CIO David Swensen, those seeking to understand tactical allocation shifts, strategy preferences, and manager selection are given little to work with on an ongoing basis.

Pension Transparency

U.S. pension funds have been plagued by chronic underfunding, decreased disclosure, and even allegations of criminal behavior. Stakeholders don’t have the option of moving their money elsewhere. MPI believes that stakeholders in public, union, and other pensions should be able to:

Validate pension disclosures and reconcile them with actual returns.
Identify the risks pension funds take.
Assess whether pension fund risk exposure has resulted in commensurate returns.
Determine whether current levels of risk are appropriate for a fund’s stated risk tolerance.

2. Why MPI?

MPI is a leader and innovator in helping large investors understand complex portfolio dynamics and the drivers of return and risk.

That’s why government regulators, top institutional investors, and investment management organizations look to MPI as part of their fund due diligence and surveillance toolbox. And it’s why investigative reporters use us to help uncover red flags (like in our analysis of a feeder fund to Bernie Madoff’s strategies.)

The same technologies that drive our products and services were recognized with best academic paper prizes. Here are some of the unique benefits MPI brings to the industry:

Strategy validation. Was it possible to achieve reported results using reported allocations? Can one trust the returns or allocations? Are there hidden risks?
A common denominator for comparing opaque investments. Every endowment or pension fund is unique in how it defines asset classes and categories of alternative investments, making apples-to-apples comparisons very difficult. Our tools can standardize allocation types across endowments, allowing a meaningful picture for comparison.
Netting exposures. Effective equity and fixed income exposures can be netted by shorting, leveraging, or the use of derivatives. Risk managers and CIOs use our factor analysis to uncover hidden exposures that truly drive returns – often unexpectedly.
Uncovering hidden allocation trends. Harvard’s restructuring after the global financial crisis, Brown’s big bet on tech, Duke’s dive into crypto. Our tools allow a sharper picture of flows in and out of allocation categories, helping reveal which funds’ behavior matches their PR.
Risk and Efficiency. Endowment and pension press often focuses on returns and ignores the risk and performance efficiency analysis every investment needs. Large endowments are long-horizon investors that lack the constraints of regulated investments. And underfunded pensions could be in danger of chasing returns through high-risk investments. MPI helps uncover an investment’s risk/return efficiency picture even with only a few public data points.
Performance attribution. Endowments had no problem boasting about asset class contributions to returns in FY2021; after all, it was a record year. But in down years, managers may be less incentivized to explain their results to stakeholders. Our tools help stakeholders form year-to-year performance attribution pictures to keep a close eye on ongoing behavior.
Meaningful investment rankings. Most public lists focus on pure performance from year to year. But MPI’s inclusion of risk and efficiency metrics helps tell more meaningful stories, shining a light, for example, on smaller endowments and pensions that are more efficient than their larger and more prominent peers.

3. Methodology

When only annual performance figures are reported, a decade’s worth of performance is represented by 10 data points. Traditional static and rolling window methods of regression analysis struggle to find credible insights from such infrequent data. MPI’s Dynamic Style Analysis (DSA), however, is uniquely able to work with such limited data.

DSA improves upon Sharpe’s original returns-based style analysis (RBSA) approach and, using factors indicative of the asset classes deployed by pensions and endowments, provides significant insights into their behavior. With our DSA, MPI is able to explain changes in an institution’s performance over time and to highlight differences across institutional portfolios using a common analytical framework.

MPI’s Dynamic Style Analysis (DSA) Methodology

Uses publicly available annual returns and public and private market indices/factors.
Captures changing portfolio exposures in ways other methodologies can’t.
Cross-validates selected factors and model parameters through rigorous machine-learning algorithms.
Computes estimated return contributions by asset both for the most recent fiscal year and the entire history.
Computes risk measures, drawdowns, stress tests and hypothetical shocks using quarterly public proxies and the most recent factor exposures.

For a much deeper dive into the MPI approach and our proprietary methodology, see this CAIA-published paper.

4. MPI-360 Reports

The takeaways from pension and endowment transparency reports found in this Hub are not much different from the ones that MPI clients derive on a daily basis when using our tools to analyze and compare individual mutual funds, hedge funds, PE and VC funds as well as portfolios of funds.

Individual MPI fund reports…

Uncover trends in asset allocation and exposures.
Explain more recent and historical results and compare them to benchmarks and peers.
Provide estimates of risks, drawdowns and efficiency
Calculate historical stress-tests and hypothetical scenarios

For a selection of many more reports, go to MPI Endowment Tracker and Pension Tracker.

Download the full Harvard FY2021 reportpdf

5. Frequently Asked Questions

Why do asset class exposures shown in MPI reports differ from the funds’ stated allocations?

Asset class exposures in our analysis represent what Prof. William Sharpe called an “effective mix” or what traders usually call “beta-adjusted exposures.” In a multi-manager, multi-strategy, and multi-asset portfolio, both equity and fixed income exposures can be affected (“netted”) by short positions, leverage and derivatives. In addition, overweight in high-beta, more aggressive strategies could result in a higher exposure to the market represented by a generic index such as the S&P 500. And, alternatively, overweight of low beta, value-focused strategies could cause effective equity exposure to be lower.

As a result, exposures obtained through factor analysis of returns may not match reported asset allocation but could provide more accurate information for both risk managers and CIOs as the effective exposures rather than the reported ones are, in fact, driving returns.

For private assets and real estate, the risk numbers in your reports seem too high. Why are they so high?

We create public market proxies to model private market returns. When we use these proxies for risk calculation, we’re not smoothing them and, therefore, they provide risk assessment as if private investments were marked to market instantaneously. It’s worth mentioning that risk estimates depend significantly on whether the estimation window includes the Financial Crisis of 2008-2009 when real estate in particular experienced a major correction in both public and private markets.

Does “Selection” in your reports represent managers’ skill or alpha?

In a multibillion dollar portfolio with dozens of managers, individual specific risk is mostly diversified away. Therefore, it’s mostly about precision of the factor model and its ability to explain composite returns rather than individual manager skill.

By definition, “Selection” is a portion of return that is not explained by our factor model. Selection might include fees, missing or imprecise factor effects, rapid changes in allocation that cannot be captured by annual data, massive write-offs or restructuring, individual success stories such as the Coinbase investment by Duke, etc. Fortunately, multibillion dollar portfolios are not easily turned over quickly, especially when significant portions are in illiquid private investments, and this makes our analyses possible and more accurate. Nevertheless, many try to see manager selection skill behind some endowment success stories. In most cases, such success is driven by a common factor we can identify. For example, we determined overweight of technology as the primary source of the Brown endowment’s winning streak.

You work with so little data - how do you know the analysis is credible?

Indeed, annual data, the only public disclosure most institutions provide, results in very small samples. Quarterly or monthly data, if available, would allow more accurate and even more granular (more factors) results. But we squeeze the most insight possible out of available data, always with a firm eye on its validity. We use machine learning techniques, including cross-validation, to calibrate our DSA model to make sure it’s not a simple data fitting exercise. These techniques are analogues to out-of-sample replication where past data with a lag is used to project next-period returns, but much more computation intensive.

As mentioned above, asset class exposures obtained through our analyses may differ from stated allocations but, if used to replicate an institution’s results out-of-sample, would deliver very close returns.

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